Lateral Phishing With Large Language Models: A Large Organization Comparative Study
The emergence of Large Language Models (LLMs) has heightened the threat of phishing emails by enabling the generation of highly targeted, personalized, and automated attacks. Traditionally, many phishing emails have been characterized by typos, errors, and poor language. These errors can be mitigate...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10943116/ |
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| author | Mazal Bethany Athanasios Galiopoulos Emet Bethany Mohammad Bahrami Karkevandi Nicole Beebe Nishant Vishwamitra Peyman Najafirad |
| author_facet | Mazal Bethany Athanasios Galiopoulos Emet Bethany Mohammad Bahrami Karkevandi Nicole Beebe Nishant Vishwamitra Peyman Najafirad |
| author_sort | Mazal Bethany |
| collection | DOAJ |
| description | The emergence of Large Language Models (LLMs) has heightened the threat of phishing emails by enabling the generation of highly targeted, personalized, and automated attacks. Traditionally, many phishing emails have been characterized by typos, errors, and poor language. These errors can be mitigated by LLMs, potentially lowering the barrier for attackers. Despite this, there is a lack of large-scale studies comparing the effectiveness of LLM-generated lateral phishing emails to those crafted by humans. Current literature does not adequately address the comparative effectiveness of LLM and human-generated lateral phishing emails in a real-world, large-scale organizational setting, especially considering the potential for LLMs to generate more convincing and error-free phishing content. To address this gap, we conducted a pioneering study within a large university, targeting its workforce of approximately 9,000 individuals including faculty, staff, administrators, and student workers. Our results indicate that LLM-generated lateral phishing emails are as effective as those written by communications professionals, emphasizing the critical threat posed by LLMs in leading phishing campaigns. We break down the results of the overall phishing experiment, comparing vulnerability between departments and job roles. Furthermore, to gather qualitative data, we administered a detailed questionnaire, revealing insights into the reasons and motivations behind vulnerable employee’s actions. This study contributes to the understanding of cyber security threats in educational institutions and provides a comprehensive comparison of LLM and human-generated phishing emails’ effectiveness, considering the potential for LLMs to generate more convincing content. The findings highlight the need for enhanced user education and system defenses to mitigate the growing threat of AI-powered phishing attacks. |
| format | Article |
| id | doaj-art-ac209f4ff3c343fa9a3d6cae09eb9cc1 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ac209f4ff3c343fa9a3d6cae09eb9cc12025-08-20T03:09:09ZengIEEEIEEE Access2169-35362025-01-0113606846070110.1109/ACCESS.2025.355550010943116Lateral Phishing With Large Language Models: A Large Organization Comparative StudyMazal Bethany0https://orcid.org/0000-0002-3227-9806Athanasios Galiopoulos1https://orcid.org/0000-0002-6747-5938Emet Bethany2https://orcid.org/0009-0003-4841-0359Mohammad Bahrami Karkevandi3https://orcid.org/0000-0001-8305-6955Nicole Beebe4https://orcid.org/0000-0002-0151-1617Nishant Vishwamitra5Peyman Najafirad6https://orcid.org/0000-0001-9671-577XDepartment of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, USADepartment of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, USASecure AI and Autonomy Laboratory, San Antonio, TX, USASecure AI and Autonomy Laboratory, San Antonio, TX, USADepartment of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, USADepartment of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, USADepartment of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, USAThe emergence of Large Language Models (LLMs) has heightened the threat of phishing emails by enabling the generation of highly targeted, personalized, and automated attacks. Traditionally, many phishing emails have been characterized by typos, errors, and poor language. These errors can be mitigated by LLMs, potentially lowering the barrier for attackers. Despite this, there is a lack of large-scale studies comparing the effectiveness of LLM-generated lateral phishing emails to those crafted by humans. Current literature does not adequately address the comparative effectiveness of LLM and human-generated lateral phishing emails in a real-world, large-scale organizational setting, especially considering the potential for LLMs to generate more convincing and error-free phishing content. To address this gap, we conducted a pioneering study within a large university, targeting its workforce of approximately 9,000 individuals including faculty, staff, administrators, and student workers. Our results indicate that LLM-generated lateral phishing emails are as effective as those written by communications professionals, emphasizing the critical threat posed by LLMs in leading phishing campaigns. We break down the results of the overall phishing experiment, comparing vulnerability between departments and job roles. Furthermore, to gather qualitative data, we administered a detailed questionnaire, revealing insights into the reasons and motivations behind vulnerable employee’s actions. This study contributes to the understanding of cyber security threats in educational institutions and provides a comprehensive comparison of LLM and human-generated phishing emails’ effectiveness, considering the potential for LLMs to generate more convincing content. The findings highlight the need for enhanced user education and system defenses to mitigate the growing threat of AI-powered phishing attacks.https://ieeexplore.ieee.org/document/10943116/Artificial intelligencecybersecuritydisinformationgenerative AIlarge language modelsphishing |
| spellingShingle | Mazal Bethany Athanasios Galiopoulos Emet Bethany Mohammad Bahrami Karkevandi Nicole Beebe Nishant Vishwamitra Peyman Najafirad Lateral Phishing With Large Language Models: A Large Organization Comparative Study IEEE Access Artificial intelligence cybersecurity disinformation generative AI large language models phishing |
| title | Lateral Phishing With Large Language Models: A Large Organization Comparative Study |
| title_full | Lateral Phishing With Large Language Models: A Large Organization Comparative Study |
| title_fullStr | Lateral Phishing With Large Language Models: A Large Organization Comparative Study |
| title_full_unstemmed | Lateral Phishing With Large Language Models: A Large Organization Comparative Study |
| title_short | Lateral Phishing With Large Language Models: A Large Organization Comparative Study |
| title_sort | lateral phishing with large language models a large organization comparative study |
| topic | Artificial intelligence cybersecurity disinformation generative AI large language models phishing |
| url | https://ieeexplore.ieee.org/document/10943116/ |
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